Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	39.784951
daal4py_KNeighborsClassifier	0.0	3.0	26.047757
KNeighborsClassifier_kd_tree	0.0	6.0	56.378027
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	45.585390
KMeans_tall	0.0	1.0	40.392747
daal4py_KMeans_tall	0.0	1.0	17.610193
KMeans_short	0.0	0.0	22.069937
daal4py_KMeans_short	0.0	0.0	9.368428
LogisticRegression	0.0	1.0	3.497449
daal4py_LogisticRegression	0.0	0.0	56.088611
Ridge	0.0	0.0	48.934829
daal4py_Ridge	0.0	0.0	15.630826
total	0.0	32.0	21.460815

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.143	0.002	1000000	1000000	100	brute	-1	1	0.99	0.987	0.498	0.001	0.287	0.003	See
1	KNeighborsClassifier	predict	25.266	0.618	1000000	1000	100	brute	-1	1	0.99	0.987	1.980	0.014	12.764	0.325	See
2	KNeighborsClassifier	predict	0.177	0.020	1000000	1	100	brute	-1	1	0.99	0.987	0.085	0.000	2.074	0.236	See
3	KNeighborsClassifier	fit	0.139	0.003	1000000	1000000	100	brute	-1	5	0.99	0.987	0.508	0.004	0.273	0.006	See
4	KNeighborsClassifier	predict	33.626	0.000	1000000	1000	100	brute	-1	5	0.99	0.987	1.990	0.007	16.899	0.059	See
5	KNeighborsClassifier	predict	0.180	0.017	1000000	1	100	brute	-1	5	0.99	0.987	0.085	0.000	2.122	0.201	See
6	KNeighborsClassifier	fit	0.143	0.002	1000000	1000000	100	brute	-1	100	0.99	0.987	0.496	0.001	0.288	0.005	See
7	KNeighborsClassifier	predict	33.850	0.000	1000000	1000	100	brute	-1	100	0.99	0.987	2.056	0.013	16.463	0.103	See
8	KNeighborsClassifier	predict	0.173	0.018	1000000	1	100	brute	-1	100	0.99	0.987	0.085	0.000	2.040	0.210	See
9	KNeighborsClassifier	fit	0.143	0.003	1000000	1000000	100	brute	1	1	0.99	0.987	0.508	0.004	0.281	0.007	See
10	KNeighborsClassifier	predict	13.283	0.106	1000000	1000	100	brute	1	1	0.99	0.987	1.983	0.011	6.698	0.065	See
11	KNeighborsClassifier	predict	0.192	0.001	1000000	1	100	brute	1	1	0.99	0.987	0.085	0.000	2.260	0.015	See
12	KNeighborsClassifier	fit	0.144	0.003	1000000	1000000	100	brute	1	5	0.99	0.987	0.496	0.001	0.289	0.006	See
13	KNeighborsClassifier	predict	22.878	0.029	1000000	1000	100	brute	1	5	0.99	0.987	1.985	0.012	11.528	0.074	See
14	KNeighborsClassifier	predict	0.200	0.002	1000000	1	100	brute	1	5	0.99	0.987	0.085	0.001	2.357	0.028	See
15	KNeighborsClassifier	fit	0.143	0.002	1000000	1000000	100	brute	1	100	0.99	0.987	0.508	0.005	0.282	0.006	See
16	KNeighborsClassifier	predict	22.754	0.107	1000000	1000	100	brute	1	100	0.99	0.987	2.047	0.008	11.118	0.067	See
17	KNeighborsClassifier	predict	0.198	0.001	1000000	1	100	brute	1	100	0.99	0.987	0.085	0.001	2.330	0.016	See
18	KNeighborsClassifier	fit	0.060	0.001	1000000	1000000	2	brute	-1	1	0.99	0.987	0.105	0.002	0.567	0.014	See
19	KNeighborsClassifier	predict	21.159	0.073	1000000	1000	2	brute	-1	1	0.99	0.987	0.310	0.002	68.215	0.443	See
20	KNeighborsClassifier	predict	0.019	0.003	1000000	1	2	brute	-1	1	0.99	0.987	0.006	0.001	3.123	0.640	See
21	KNeighborsClassifier	fit	0.060	0.001	1000000	1000000	2	brute	-1	5	0.99	0.987	0.104	0.001	0.580	0.010	See
22	KNeighborsClassifier	predict	32.504	0.000	1000000	1000	2	brute	-1	5	0.99	0.987	0.313	0.002	103.797	0.690	See
23	KNeighborsClassifier	predict	0.034	0.004	1000000	1	2	brute	-1	5	0.99	0.987	0.006	0.001	5.523	0.936	See
24	KNeighborsClassifier	fit	0.059	0.001	1000000	1000000	2	brute	-1	100	0.99	0.987	0.105	0.003	0.564	0.020	See
25	KNeighborsClassifier	predict	32.342	0.000	1000000	1000	2	brute	-1	100	0.99	0.987	0.373	0.010	86.814	2.223	See
26	KNeighborsClassifier	predict	0.033	0.002	1000000	1	2	brute	-1	100	0.99	0.987	0.006	0.001	5.163	0.908	See
27	KNeighborsClassifier	fit	0.071	0.002	1000000	1000000	2	brute	1	1	0.99	0.987	0.104	0.001	0.678	0.022	See
28	KNeighborsClassifier	predict	10.346	0.029	1000000	1000	2	brute	1	1	0.99	0.987	0.308	0.003	33.623	0.323	See
29	KNeighborsClassifier	predict	0.015	0.001	1000000	1	2	brute	1	1	0.99	0.987	0.007	0.001	2.215	0.312	See
30	KNeighborsClassifier	fit	0.070	0.002	1000000	1000000	2	brute	1	5	0.99	0.987	0.105	0.003	0.666	0.028	See
31	KNeighborsClassifier	predict	21.542	0.011	1000000	1000	2	brute	1	5	0.99	0.987	0.309	0.003	69.632	0.658	See
32	KNeighborsClassifier	predict	0.027	0.001	1000000	1	2	brute	1	5	0.99	0.987	0.006	0.001	4.324	0.814	See
33	KNeighborsClassifier	fit	0.070	0.002	1000000	1000000	2	brute	1	100	0.99	0.987	0.104	0.001	0.670	0.023	See
34	KNeighborsClassifier	predict	21.656	0.083	1000000	1000	2	brute	1	100	0.99	0.987	0.366	0.002	59.173	0.430	See
35	KNeighborsClassifier	predict	0.027	0.001	1000000	1	2	brute	1	100	0.99	0.987	0.006	0.000	4.450	0.370	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	2.966	0.052	1000000	1000000	10	kd_tree	-1	1	0.99	0.985	0.726	0.013	4.087	0.101	See
1	KNeighborsClassifier_kd_tree	predict	0.461	0.002	1000000	1000	10	kd_tree	-1	1	0.99	0.985	0.103	0.001	4.455	0.056	See
2	KNeighborsClassifier_kd_tree	predict	0.006	0.005	1000000	1	10	kd_tree	-1	1	0.99	0.985	0.000	0.000	13.392	11.622	See
3	KNeighborsClassifier_kd_tree	fit	2.979	0.027	1000000	1000000	10	kd_tree	-1	5	0.99	0.985	0.755	0.004	3.943	0.042	See
4	KNeighborsClassifier_kd_tree	predict	0.878	0.004	1000000	1000	10	kd_tree	-1	5	0.99	0.985	0.184	0.001	4.775	0.044	See
5	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	-1	5	0.99	0.985	0.001	0.000	6.563	2.735	See
6	KNeighborsClassifier_kd_tree	fit	2.930	0.032	1000000	1000000	10	kd_tree	-1	100	0.99	0.985	0.726	0.005	4.034	0.052	See
7	KNeighborsClassifier_kd_tree	predict	2.862	0.010	1000000	1000	10	kd_tree	-1	100	0.99	0.985	0.549	0.004	5.212	0.044	See
8	KNeighborsClassifier_kd_tree	predict	0.006	0.001	1000000	1	10	kd_tree	-1	100	0.99	0.985	0.001	0.000	5.037	1.966	See
9	KNeighborsClassifier_kd_tree	fit	3.002	0.024	1000000	1000000	10	kd_tree	1	1	0.99	0.985	0.752	0.004	3.990	0.039	See
10	KNeighborsClassifier_kd_tree	predict	0.742	0.005	1000000	1000	10	kd_tree	1	1	0.99	0.985	0.103	0.001	7.207	0.089	See
11	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	0.99	0.985	0.000	0.000	2.723	1.175	See
12	KNeighborsClassifier_kd_tree	fit	3.012	0.023	1000000	1000000	10	kd_tree	1	5	0.99	0.985	0.734	0.019	4.102	0.108	See
13	KNeighborsClassifier_kd_tree	predict	1.424	0.009	1000000	1000	10	kd_tree	1	5	0.99	0.985	0.183	0.001	7.784	0.065	See
14	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	1	5	0.99	0.985	0.001	0.000	3.467	1.495	See
15	KNeighborsClassifier_kd_tree	fit	3.006	0.023	1000000	1000000	10	kd_tree	1	100	0.99	0.985	0.751	0.004	4.001	0.036	See
16	KNeighborsClassifier_kd_tree	predict	4.736	0.025	1000000	1000	10	kd_tree	1	100	0.99	0.985	0.552	0.002	8.586	0.056	See
17	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	1	100	0.99	0.985	0.001	0.000	3.283	1.331	See
18	KNeighborsClassifier_kd_tree	fit	1.391	0.008	1000000	1000000	2	kd_tree	-1	1	0.99	0.985	0.509	0.013	2.735	0.070	See
19	KNeighborsClassifier_kd_tree	predict	0.030	0.001	1000000	1000	2	kd_tree	-1	1	0.99	0.985	0.001	0.000	30.815	14.696	See
20	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	1	0.99	0.985	0.000	0.000	19.684	14.346	See
21	KNeighborsClassifier_kd_tree	fit	1.405	0.008	1000000	1000000	2	kd_tree	-1	5	0.99	0.985	0.498	0.004	2.819	0.025	See
22	KNeighborsClassifier_kd_tree	predict	0.031	0.001	1000000	1000	2	kd_tree	-1	5	0.99	0.985	0.001	0.001	22.428	9.768	See
23	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	5	0.99	0.985	0.000	0.000	21.526	16.404	See
24	KNeighborsClassifier_kd_tree	fit	1.393	0.008	1000000	1000000	2	kd_tree	-1	100	0.99	0.985	0.507	0.013	2.746	0.073	See
25	KNeighborsClassifier_kd_tree	predict	0.049	0.001	1000000	1000	2	kd_tree	-1	100	0.99	0.985	0.007	0.001	6.788	0.789	See
26	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	100	0.99	0.985	0.000	0.000	19.393	13.409	See
27	KNeighborsClassifier_kd_tree	fit	1.389	0.008	1000000	1000000	2	kd_tree	1	1	0.99	0.985	0.510	0.011	2.725	0.062	See
28	KNeighborsClassifier_kd_tree	predict	0.027	0.001	1000000	1000	2	kd_tree	1	1	0.99	0.985	0.001	0.000	22.038	6.808	See
29	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	0.99	0.985	0.000	0.000	5.698	3.995	See
30	KNeighborsClassifier_kd_tree	fit	1.388	0.007	1000000	1000000	2	kd_tree	1	5	0.99	0.985	0.499	0.005	2.779	0.034	See
31	KNeighborsClassifier_kd_tree	predict	0.030	0.001	1000000	1000	2	kd_tree	1	5	0.99	0.985	0.001	0.000	25.086	6.316	See
32	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	0.99	0.985	0.000	0.000	5.663	3.753	See
33	KNeighborsClassifier_kd_tree	fit	1.396	0.009	1000000	1000000	2	kd_tree	1	100	0.99	0.985	0.501	0.006	2.783	0.037	See
34	KNeighborsClassifier_kd_tree	predict	0.052	0.003	1000000	1000	2	kd_tree	1	100	0.99	0.985	0.008	0.002	6.566	1.410	See
35	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	0.99	0.985	0.000	0.000	5.366	3.444	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.638	0.007	1000000	1000000	2	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	0.476	0.021	1.340	0.060	See
1	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	1.999	1.218	See
2	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	2.271	1.796	See
3	KMeans_tall	fit	0.555	0.004	1000000	1000000	2	full	random	30	3	1	0.0	30	0.002	30	0.002	0.428	0.017	1.296	0.052	See
4	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	2.009	1.299	See
5	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	2.382	1.850	See
6	KMeans_tall	fit	6.574	0.087	1000000	1000000	100	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	3.043	0.017	2.160	0.031	See
7	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	2.157	1.065	See
8	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	1.964	1.356	See
9	KMeans_tall	fit	6.003	0.018	1000000	1000000	100	full	random	30	3	1	0.0	30	0.002	30	0.002	2.875	0.008	2.088	0.008	See
10	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	2.240	1.018	See
11	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	1.884	1.294	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.341	0.013	10000	10000	2	full	k-means++	30	300	1	0.0	30	0.003	30	0.005	0.110	0.003	3.103	0.146	See
1	KMeans_short	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	30	0.003	30	0.005	0.001	0.000	1.164	0.439	See
2	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	30	0.003	30	0.005	0.000	0.000	2.185	1.450	See
3	KMeans_short	fit	0.129	0.001	10000	10000	2	full	random	30	300	1	0.0	30	0.003	30	0.005	0.049	0.001	2.619	0.052	See
4	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	30	0.003	30	0.005	0.001	0.000	1.131	0.433	See
5	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	30	0.003	30	0.005	0.000	0.000	2.307	1.725	See
6	KMeans_short	fit	0.829	0.029	10000	10000	100	full	k-means++	30	300	1	0.0	16	0.003	17	0.005	0.373	0.024	2.226	0.161	See
7	KMeans_short	predict	0.003	0.002	10000	1000	100	full	k-means++	30	300	1	0.0	16	0.003	17	0.005	0.001	0.000	1.998	1.387	See
8	KMeans_short	predict	0.000	0.000	10000	1	100	full	k-means++	30	300	1	0.0	16	0.003	17	0.005	0.000	0.000	1.708	1.062	See
9	KMeans_short	fit	0.246	0.018	10000	10000	100	full	random	30	300	1	0.0	25	0.003	20	0.005	0.167	0.020	1.467	0.209	See
10	KMeans_short	predict	0.003	0.001	10000	1000	100	full	random	30	300	1	0.0	25	0.003	20	0.005	0.001	0.000	2.225	1.141	See
11	KMeans_short	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	25	0.003	20	0.005	0.000	0.000	1.699	0.969	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	11.247	0.014	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	0.2	11.287	0.050	0.996	0.005	See
1	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	0.2	0.000	0.000	0.815	0.496	See
2	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	0.2	0.000	0.000	0.309	0.273	See
3	LogisticRegression	fit	0.795	0.012	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[28]	0.2	0.772	0.020	1.030	0.030	See
4	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[28]	0.2	0.003	0.001	0.546	0.139	See
5	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[28]	0.2	0.001	0.000	0.123	0.077	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	1.759	0.044	100000	100000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.940	0.003	1.870	0.047	See
1	Ridge	predict	0.001	0.000	100000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.001	0.000	0.829	0.425	See
2	Ridge	predict	0.000	0.000	100000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.000	0.000	0.633	0.647	See
3	Ridge	fit	1.173	0.015	1000000	1000000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.242	0.002	4.837	0.070	See
4	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.000	0.000	0.723	0.553	See
5	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.000	0.000	0.616	0.642	See

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.2",
    "numpy": "1.20.2",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}